Measurement of a dimension of a physiological tissue, such as a mammalian tissue, can have important clinical and research applications in a variety of diagnostic and therapeutic fields. For example, measurement of corneal thickness may have applications in the diagnosis and/or treatment of conditions in the field of optometry or ophthalmology such as glaucoma, corneal pathology, refractive surgery and contact lenses. However, despite strong associations among measurements of central corneal thickness by different techniques,1-6 there is a lack of a gold standard for cross-calibration between different instruments.
Although there is abundant literature on precision (repeatability or reliability) of the common biometric equipment for measuring different tissues including corneal thickness3,6-21, no information about accuracy of the methods exists. Precision quantifies how multiple measures compare with each other. Accuracy is an indicator of the proximity of the measurement to the real physical value that is being measured. A measurement method could be precise but not accurate.22,23 For example, a piece of equipment could always underestimate corneal thickness by, say, 40 μm and be very precise (repeatable) for this measurement, which is not accurate. However, the question is whether a refractive surgeon or a glaucoma specialist can make a sound clinical decision based on this measurement, particularly in borderline cases. Therefore, in addition to the importance of precision, a measurement technique should also be accurate and its calibration should be verifiable using a gold standard.
Currently, a non-invasive method for comparing tissue measurements taken by different biometric devices does not exist. Such a comparison can only be conducted by obtaining a sample of the subject tissue, for example, by biopsy. This method of comparison is neither acceptable nor feasible in the case of certain tissue types such as the ocular tissue.
It would, thus, be desirable to develop a method of using a biometric device which renders accurate results that can be validly compared with similar results obtained using different devices.